Machine Learning Software Engineer

Wayve • Sunnyvale, CA

16h • Hybrid

About The Position

Wayve is seeking an ML Software Engineer to join the Multi-Embodiment Generalist Agent (MEGA) team within Wayve Science as a founding member. MEGA is developing foundation models for general-purpose robots that learn from large-scale video, language, and robot-interaction data and generalize across tasks and embodiments, including mobile manipulators, dual-arm platforms, and humanoids. The team aims to build agents that can perceive, reason about, and act reliably in the physical world.

The role designs and builds the software systems that enable MEGA’s machine learning research. This includes scalable ML pipelines, data and training infrastructure, and shared tools and abstractions that help researchers move from ideas to reliable experiments. The engineer will work closely with models and data, using large-scale video and robotics datasets, modern ML frameworks, and increasingly large models to build reliable, maintainable systems.

As a core team member, the engineer will own key parts of the software and ML infrastructure underpinning the research program and help shape how the team develops and operates its ML stack as the program grows. The role works closely with researchers and engineers to translate evolving research needs into practical software systems.

Requirements

• Strong software engineering skills and experience building high-quality, maintainable software.
• Experience building and maintaining machine learning pipelines or infrastructure, such as data ingestion, training, evaluation, or experiment workflows.
• Experience designing reliable, reusable software systems and abstractions that can evolve as requirements change.
• Hands-on experience with modern machine learning frameworks and a good understanding of ML training and experimentation workflows.
• Strong debugging skills and ability to investigate problems across complex ML systems.
• Experience with software testing, code quality, and engineering practices for maintaining a healthy shared codebase.
• Experience working with large datasets, large models, or other computationally demanding ML workloads.
• Ability to collaborate closely with researchers and engineers and translate research requirements into practical software systems.

Responsibilities

• Design, build, and maintain scalable pipelines for data ingestion, model training, evaluation, and experimentation.
• Build software systems that support scaling ML workloads to larger datasets, models, and numbers of experiments.
• Develop reusable interfaces and shared tools across data, models, training, evaluation, and downstream robotics workflows.
• Own and improve the MEGA codebase, including its architecture, testing, reliability, maintainability, and engineering standards.
• Identify and resolve performance, reliability, and usability bottlenecks across ML workflows.
• Build and maintain infrastructure that supports multiple researchers and ML projects without unnecessarily slowing iteration.
• Work with researchers to understand new model and experiment requirements and translate them into practical software solutions.
• Build and use distributed training and data-processing pipelines for large models and multimodal datasets.
• Support data processing and distributed training across large video, language, and robot-interaction datasets.

Benefits

• Market-benchmarked salaries, reviewed annually.
• Meaningful equity.
• Relocation support and visa sponsorship where applicable.
• Hybrid working, core hours, and opportunities to work hands-on in vehicle workshops and labs.
• Learning and development budgets, with support for training, conferences, and growth.
• Comprehensive benefits including health insurance, dental insurance, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, and team socials.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed